1. Field of the Invention
This invention relates generally to pattern recognition, and more specifically to a system and method for ranking and extracting salient contours for improved target recognition.
2. Description of the Related Art
The central problem in machine vision and automatic target recognition (ATR) is to obtain robust descriptions from images. Robust descriptions help not only in object recognition, but also in motion matching, stereo matching, as well as in generally reducing image clutter. For object recognition, in particular, good descriptions increase the probability of recognition and reduce the probability of false alarms.
Edge detectors have been developed to attempt to obtain such robust descriptions. These detectors usually have thresholds to control which edges they find in the image. For example, the Canny edge detector has two thresholds controlling which edges are retained and which are thrown away. However, because the thresholds in the Canny edge detector are based on just the strengths of the edges, the resulting edges are not salient contours corresponding to object boundaries. On the other hand, desired object boundaries may not necessarily be strong but may have other important characteristics.
Other systems have been developed which employ total curvature as well as total curvature variation. Such systems obviously prefer circles and edges with substantial curvature. Additionally, such systems interactively converge to a solution and require a network to compute saliency
A method recently developed extracts salient axes of symmetry called a skeleton sketch, although the method does not work with real images and does not find salient contours. Another method using indoor images (which have significantly less clutter and imperfection compared to outdoor images) employs a perceptual organization for grouping edges based on colinearity, parallelism, and co-termination Such a method is not robust enough for outdoor images.
Other related research involves methods for intensity based region segmentation. These techniques group image pixels into regions based on some similiarity measure. There are several problems with these methods, such as the fact they always yield closed boundaries. In complex, real outdoor images closed boundaries usually correspond to highlights, shadows, etc., but rarely correspond to object boundaries. Another problem is that the region boundaries are extremely sensitive to thresholds, (i.e., such boundaries change and move significantly as thresholds change). Yet another problem is that region segmentation is a global operation, meaning it is less sensitive and more likely to miss low contrast boundaries or small objects, particularly in complex images. For example, long, thin objects may be missed, although they may be salient.
Accordingly, improvements which overcome any or all of these problems are presently desirable.